zhivex-ai-sdk 0.9.0

Agent-first Python SDK for building orchestrated AI systems across multiple providers

Zhivex AI SDK for Python

Zhivex AI SDK for Python is an async-first, agent-first SDK for building orchestrated AI systems across multiple providers.

It brings the same design goals as the TypeScript Zhivex AI SDK into Python:

• one agent runtime with executable handoffs, native subagent tools, shared sessions, durable run state, evaluation helpers, safety policies, tool registries, and traces
• one normalized foundation layer for text generation, streaming, tools, structured output, embeddings, audio, grounded text, and routing
• thin provider adapters instead of provider-specific app logic everywhere
• portable application code across the portable tier, with explicit native escape hatches for provider-specific features

Why Zhivex AI SDK

Modern AI apps usually start simple and then drift into provider lock-in:

• OpenAI requests look one way
• Anthropic uses a different message format
• Gemini and Vertex differ again
• local and routed setups add yet another layer

Zhivex AI SDK gives you a common agent runtime and model contract so your application code can stay stable while providers change underneath.

Stability And Support

Zhivex AI SDK is now published as a beta package with a documented stable surface, explicit stability levels, and versioning rules for downstream integrators.

Production integrations should import supported APIs from zhivex_ai, prefer the documented stable surface and tier-1 providers, and isolate beta or experimental areas behind an application-owned service layer.

See STABILITY.md, VERSIONING.md, SUPPORT.md, and CHANGELOG.md for the contract that governs public API expectations, support scope, and release communication.

Start Here

• New backend setup: docs/QUICKSTART.md
• Provider setup and smoke checks: docs/PROVIDERS.md
• Agent runtime: docs/AGENTS.md
• Declarative workflows: docs/WORKFLOWS.md
• Production API patterns: PRODUCTION_APIS.md
• Gateway routing: docs/GATEWAY.md
• Observability: docs/OBSERVABILITY.md
• Operations runbook: docs/OPERATIONS.md
• Security: SECURITY.md
• Troubleshooting: docs/TROUBLESHOOTING.md
• Parity and GA boundary: docs/PARITY_MATRIX.md
• Contribution workflow: CONTRIBUTING.md
• Local environment template: .env.example

Highlights

• Agent runtime with executable handoffs, native subagent tools, input/output guardrails, registry-based orchestration, durable run state, transcript + summary memory, permission-aware tool execution, and traces
• Declarative workflow agents with SequentialAgent, ParallelAgent, LoopAgent, shared session.state, output_key, and templated step inputs
• AgentRuntime, AgentRegistry, and ToolRegistry as the primary orchestration layer
• Unified generate_text() and stream_text() foundation primitives
• Structured output with generate_object() and stream_object()
• Grounded text for providers with web search support
• Audio transcription and speech generation where the provider supports it
• Experimental realtime/live voice sessions plus stream_live_agent() for voice-first agents
• Text and multimodal embeddings support where the provider supports it
• Google native clients for Gemini/Vertex image, video, music, batch, interaction, and Gemini context-cache workflows
• Provider factories for hosted and local models
• Beta provider agent capability metadata for support tiers and hosted-agent features
• Beta first-class hosted tool definitions for provider-native tools in tools={...}
• Beta typed provider-data payloads and approval flows for OpenAI/Azure remote MCP
• Beta response-reference helpers and provider-data UI chunks for OpenAI/Azure continuation workflows
• Gateway routing with fallback support
• Production-style API and worker examples for durable agents, idempotency, gateway attempts, and release evidence
• HTTP/UI helpers for SSE, plain text streams, and UI message transport
• Middleware for telemetry, caching, and circuit breaking
• Model catalog helpers for cost and recommendation metadata
• Platform helpers for replay, evaluation reports, hierarchical run traces, budget guards, redaction, and run cancellation
• Offline reference apps for business workflows, including small-business loan and HR candidate selection flows that demonstrate repair/resume, human approval, fairness checks, trace replay, and app-owned storage without requiring provider credentials

Supported Providers

Provider factories now return a ProviderBundle with two explicit namespaces:

• provider("model-id") or provider.portable.language_model("model-id") for the strict portable contract
• provider.native.language_model("model-id") for provider-specific options, hosted tools, and escape hatches

Portable construction fails fast for providers that do not satisfy the portable contract. Those providers remain available through provider.native.

For production API work, the current tier-1 provider story for the stable surface is OpenAI, Anthropic, Azure OpenAI, Gemini, Vertex, Qwen, Kimi/Moonshot, and vLLM. Other providers remain available, but their supported feature set should be evaluated against the matrix below and the stability definitions in STABILITY.md.

This matrix is generated from runtime support metadata via scripts/generate_support_matrix.py. Regenerate the README block with python3 scripts/generate_support_matrix.py --write-readme. It includes beta provider agent capability metadata alongside portable support and native extras, so the docs stay aligned with the runtime support model instead of drifting by hand.

Tier-1 Providers

These providers back the stable surface for production API work in this SDK today:

• openai
• anthropic
• azure-openai
• gemini
• vertex
• qwen
• kimi
• vllm

Portable Support

Provider	Tier	Portable Badge	Text	Streaming	Structured Output	Tools	Embeddings	Grounding	Retrieval	Transcription	Speech
anthropic	portable	Yes	Yes	Yes	Yes	Yes	No	Yes	Yes	No	No
azure-openai	portable	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
bedrock	native-only	No	Yes	No	No	No	No	No	Yes	No	No
gemini	portable	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
kimi	portable	Yes	Yes	Yes	Yes	Yes	No	No	No	No	No
ollama	compatibility	No	Yes	Yes	Yes	Yes	Yes	No	Yes	No	No
openai	portable	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
openrouter	native-only	No	Yes	Yes	Yes	Yes	Yes	No	Yes	No	Yes
qwen	portable	Yes	Yes	Yes	Yes	Yes	Yes	No	No	No	No
vertex	portable	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
vllm	portable	Yes	Yes	Yes	Yes	Yes	Yes	No	Yes	Yes	No

Native Extras

Provider	Text	Streaming	Structured Output	Tools	Files	File Search	Images	Uploads	Moderations	Batches	Videos	Media	Interactions	Containers	Skills	Realtime	Responses	Conversations	Caches
anthropic	Yes	Yes	Yes	Yes	Yes	No	No	No	No	No	No	No	No	No	No	No	No	No	No
azure-openai	Yes	Yes	Yes	Yes	No	Yes	No	No	No	No	No	No	No	No	No	Yes	Yes	Yes	No
bedrock	Yes	Yes	No	Yes	No	No	No	No	No	No	No	No	No	No	No	Yes	No	No	No
gemini	Yes	Yes	Yes	Yes	Yes	Yes	Yes	No	No	Yes	Yes	Yes	Yes	No	No	Yes	No	No	Yes
kimi	Yes	Yes	Yes	Yes	Yes	No	No	No	No	Yes	No	No	No	No	No	No	No	No	No
ollama	Yes	Yes	Yes	Yes	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No
openai	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	No	No	No	Yes	Yes	Yes	Yes	Yes	No
openrouter	Yes	Yes	Yes	Yes	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No
qwen	Yes	Yes	Yes	Yes	Yes	No	No	No	No	Yes	No	No	No	No	No	No	Yes	No	No
vertex	Yes	Yes	Yes	Yes	No	No	Yes	No	No	No	Yes	Yes	No	No	No	Yes	No	No	No
vllm	Yes	Yes	Yes	Yes	No	No	No	No	No	No	No	No	No	No	No	Yes	No	No	No

Agent Capabilities

Provider	Support Tier	Tool Choice None	Approval Requests	Hosted Web Search	Hosted File Search	Remote MCP	Computer Use	Code Execution	Toolsets
anthropic	tier-b	Yes	No	Yes	No	No	No	Yes	Yes
azure-openai	tier-a	Yes	Yes	Yes	Yes	Yes	Yes	No	No
bedrock	tier-b	Yes	No	No	No	No	No	No	No
gemini	tier-b	Yes	No	Yes	Yes	No	Yes	Yes	No
kimi	tier-b	Yes	No	No	No	No	No	No	Yes
ollama	tier-c	No	No	No	No	No	No	No	No
openai	tier-a	Yes	Yes	Yes	Yes	Yes	Yes	No	No
openrouter	tier-c	Yes	No	Yes	No	No	No	No	No
qwen	tier-b	Yes	No	Yes	Yes	Yes	No	Yes	No
vertex	tier-b	Yes	No	Yes	Yes	No	Yes	Yes	No
vllm	tier-b	Yes	No	No	No	No	No	No	No

Tool Calling Notes

Tool support now follows the same rule everywhere:

• The portable layer accepts only the SDK-owned contract. It rejects provider_options and any provider-managed tool payloads.
• Provider-specific hosted tools, raw Responses settings, Gemini built-in tools, and similar knobs must go through provider.native.*.
• First-class hosted tool definitions are the preferred native path for OpenAI, Azure OpenAI, Gemini, Vertex, and Anthropic. Legacy raw provider_options payloads remain accepted where already supported for backward compatibility.
• Hosted tools now fail fast in the shared foundation layer when they target the wrong provider, request an unsupported hosted-tool class, or use hosted-only combinations such as unsupported tool_choice="none" / ToolChoiceName(...).
• The Agent Capabilities table above is beta metadata. It documents the current hosted-tool and provider-managed approval story, but it is not a stable promise that every provider will keep identical semantics release to release.
• Tier-1 support means the provider participates in the stable surface story, production API examples, and contract-level support assertions in this repository.
• Anthropic is part of the tier-1 text-generation story in this SDK. Claude Fable 5 is available as claude-fable-5, while Claude Opus 4.8 remains available as claude-opus-4-8. Use ReasoningConfig(effort="medium" | "high" | "xhigh" | "max") to send output_config.effort, with adaptive thinking automatically selected for Fable 5 and Opus 4.7/4.8. Extended thinking still restricts tool_choice to auto or none, and embeddings, transcription, and speech remain unavailable on the Anthropic provider path here.
• Claude Opus 4.8 accepts mid-conversation ModelMessage(role="system", ...) entries on the Anthropic native path when they follow Anthropic's placement rules. Fast mode remains a native escape hatch through provider_options={"speed": "fast"}.
• Anthropic stop_reason="refusal" is normalized as finish_reason="refusal" while preserving provider_finish_reason. Gateway routes use fallback_on_refusal=False by default so a primary refusal is not re-sent to fallbacks unless the route explicitly opts in with fallback_on_refusal=True.
• Anthropic hosted-tool defaults remain backward-compatible: anthropic_web_search_tool() emits web_search_20250305, anthropic_code_execution_tool() emits code_execution_20250825, and anthropic_mcp_server() uses mcp-client-2025-04-04. Current Anthropic MCP can be opted into with anthropic_mcp_server(..., version="current") or provider_options={"anthropic_mcp_beta": "mcp-client-2025-11-20"}. Current web search can be opted into with anthropic_web_search_tool(tool_type="web_search_20260209"); newer code-execution tool versions are model-dependent and should be passed explicitly when needed.
• OpenAI, Anthropic, Azure OpenAI, Gemini, Vertex, Qwen, Kimi, and vLLM now participate in the tier-1 portable text-generation contract.
• vLLM is tier-1 for the SDK primitives backed by its OpenAI-compatible server. Embeddings, transcription, and realtime ASR depend on serving compatible model tasks in vLLM; vLLM custom endpoints such as tokenize, rerank, classify, and score are not SDK APIs yet.
• Azure OpenAI hosted-tool helpers map OpenAI-style tool payloads for native model calls, and the Azure provider bundle now mirrors the beta native lifecycle clients for vector-store/file-search administration, Responses, and Conversations through /openai/v1.
• Gemini and Vertex are portable for the core contract, but Gemini built-in tools such as google_search, google_maps, url_context, code_execution, file_search, and computer_use are native-only entrypoints.
• Gemini function-calling preserves Google functionCall.id / functionResponse.id for Gemini 3 tool loops, while continuing to preserve thoughtSignature for reasoning-aware tool handoffs.
• Gateway routing emits on_attempt payloads for skipped targets as well as executed attempts. Use GatewayConfig(fail_on_missing_adapter=True) for production routes where a missing provider adapter should fail fast instead of falling through to a fallback.
• Bedrock, OpenRouter, and Ollama remain available, but only through provider.native until they satisfy the portable contract end to end.
• Qwen follows Alibaba Cloud Model Studio's current OpenAI-compatible split: portable text/streaming/tools/structured output run through Responses, embeddings run through the OpenAI-compatible endpoint, and hosted web/code/file/MCP tools remain native/provider-specific. create_qwen() accepts either QWEN_API_KEY or the official DASHSCOPE_API_KEY, and the catalog tracks Qwen3.7 Max/Plus alongside the Qwen3.6/3.5 families.
• Qwen native support includes raw provider.responses(), Files, Batch, Qwen3-ASR, and DashScope TTS. File Search is exposed as a hosted Responses tool with vector_store_ids, not as a lifecycle client.
• Kimi/Moonshot uses the official Chat Completions route for portable text generation, streaming, structured output, and callable tools. create_kimi() reads MOONSHOT_API_KEY first, then KIMI_API_KEY, and defaults to https://api.moonshot.ai/v1.
• Kimi native support includes Moonshot Files, Batch, token estimation, K2.6/K2.5 thinking, image/video chat inputs, and the beta provider.formulas() client for official tools such as moonshot/web-search:latest; embeddings, speech, and transcription are not claimed for Kimi.
• Ollama defaults to base_url="http://localhost:11434/v1" and api_key="ollama" for local compatibility setups. Use provider.native.* for Ollama examples and override OLLAMA_API_KEY only when you front it with a proxy or remote gateway that requires auth.

Installation

pip install zhivex-ai-sdk

Optional extras:

pip install "zhivex-ai-sdk[postgres]"
pip install "zhivex-ai-sdk[mcp]"
pip install "zhivex-ai-sdk[api]"
pip install "zhivex-ai-sdk[otel]"
pip install "zhivex-ai-sdk[docx]"

The beta skill-package layer also exposes a CLI:

zhivex-skills validate path/to/skill
zhivex-skills install path/to/skill

Quick Start

import asyncio

from zhivex_ai import Agent, create_in_memory_agent_memory_store, create_openai, run_agent


async def main() -> None:
    openai = create_openai()
    agent = Agent(
        name="assistant",
        instructions="Be concise and remember prior turns.",
        model=openai("gpt-5.4-mini"),
        memory=create_in_memory_agent_memory_store(),
    )

    first = await run_agent(agent=agent, prompt="Remember that project Apollo is important.")
    second = await run_agent(agent=agent, session=first.session, prompt="What project did I mention?")

    print(second.text)


asyncio.run(main())

Foundation APIs

Text generation

import asyncio

from zhivex_ai import create_openai, generate_text


async def main() -> None:
    openai = create_openai()

    result = await generate_text(
        model=openai("gpt-5.4-mini"),
        system="Be concise and technical.",
        prompt="What is a provider adapter?",
    )

    print(result.text)


asyncio.run(main())

Structured output

import asyncio

from pydantic import BaseModel

from zhivex_ai import create_openai, generate_object


class Recipe(BaseModel):
    title: str
    difficulty: str


async def main() -> None:
    openai = create_openai()

    result = await generate_object(
        model=openai("gpt-5.4-mini"),
        prompt="Return a compact JSON recipe summary.",
        schema=Recipe,
    )

    print(result.object.model_dump())


asyncio.run(main())

Streaming

import asyncio

from zhivex_ai import create_openai, stream_text


async def main() -> None:
    openai = create_openai()
    result = stream_text(
        model=openai("gpt-5.4-mini"),
        prompt="Reply in two short sentences.",
    )

    async for chunk in result.text_stream():
        print(chunk, end="")

    final = await result.collect()
    print("\n", final.finish_reason)


asyncio.run(main())

Structured output streaming

import asyncio

from pydantic import BaseModel

from zhivex_ai import create_openai, stream_object


class Recipe(BaseModel):
    title: str
    servings: int


async def main() -> None:
    openai = create_openai()
    result = stream_object(
        model=openai("gpt-5.4-mini"),
        prompt="Return a compact JSON recipe.",
        schema=Recipe,
    )

    async for partial in result.partial_object_stream():
        print(partial)

    final = await result.collect()
    print(final.object.model_dump())


asyncio.run(main())

Grounded text

import asyncio

from zhivex_ai import create_openai, generate_grounded_text


async def main() -> None:
    openai = create_openai()

    result = await generate_grounded_text(
        model=openai.grounded_language_model("gpt-5.4-mini"),
        prompt="Find one recent fact about AI infrastructure.",
    )

    print(result.text)
    for source in result.sources:
        print(source.title, source.url)


asyncio.run(main())

Anthropic grounding is also available on the portable tier:

import asyncio

from zhivex_ai import create_anthropic, generate_grounded_text


async def main() -> None:
    anthropic = create_anthropic()

    result = await generate_grounded_text(
        model=anthropic.grounded_language_model("claude-sonnet-4-20250514"),
        prompt="Find one recent fact about AI infrastructure.",
    )

    print(result.text)
    for source in result.sources:
        print(source.title, source.url)


asyncio.run(main())

Gemini and Vertex grounding are also explicit and opt-in:

import asyncio

from zhivex_ai import create_gemini, generate_grounded_text


async def main() -> None:
    gemini = create_gemini()

    result = await generate_grounded_text(
        model=gemini.grounded_language_model("gemini-2.5-flash"),
        prompt="Find one recent fact about AI infrastructure.",
    )

    print(result.text)
    for source in result.sources:
        print(source.title, source.url)


asyncio.run(main())

Files and multimodal input

FilePart is no longer PDF-only. Gemini accepts inline or uploaded files for documents, audio, images, and video. Vertex accepts inline files plus URI-based file references such as gs://....

Inline document input:

import asyncio

from zhivex_ai import FilePart, ModelMessage, TextPart, create_openai, generate_text


async def main() -> None:
    openai = create_openai()
    result = await generate_text(
        model=openai("gpt-5.4-mini"),
        messages=[
            ModelMessage(
                role="user",
                parts=[
                    FilePart(
                        data="JVBERi0xLjQK",
                        media_type="application/pdf",
                        filename="statement.pdf",
                    ),
                    TextPart(text="Summarize this PDF in three bullets."),
                ],
            )
        ],
    )

    print(result.text)


asyncio.run(main())

Reusing a previously uploaded Anthropic file through the native file flow:

import asyncio

from zhivex_ai import FilePart, ModelMessage, create_anthropic, generate_text


async def main() -> None:
    anthropic = create_anthropic()
    result = await generate_text(
        model=anthropic.native.language_model("claude-sonnet-4-20250514"),
        messages=[
            ModelMessage(
                role="user",
                parts=[FilePart(file_id="file_123"), FilePart(file_id="file_456")],
            )
        ],
    )

    print(result.text)


asyncio.run(main())

Using the Gemini Files API first, then passing the returned reference:

import asyncio

from zhivex_ai import FilePart, ModelMessage, TextPart, create_gemini, generate_text


async def main() -> None:
    gemini = create_gemini()
    uploaded = await gemini.files().upload(
        data=b"%PDF-1.4...",
        filename="statement.pdf",
    )

    result = await generate_text(
        model=gemini("gemini-2.5-flash"),
        messages=[
            ModelMessage(
                role="user",
                parts=[
                    FilePart(file_uri=uploaded.file_uri),
                    TextPart(text="Extract the key numbers from this statement."),
                ],
            )
        ],
    )

    print(result.text)


asyncio.run(main())

Embedding multimodal Gemini content:

import asyncio

from zhivex_ai import FilePart, TextPart, create_gemini, embed_content


async def main() -> None:
    gemini = create_gemini()
    result = await embed_content(
        model=gemini.native.embedding_model("gemini-embedding-2"),
        value=[
            TextPart(text="Find visually similar documents."),
            FilePart(data="JVBERi0xLjQK", media_type="application/pdf", filename="brief.pdf"),
        ],
    )

    print(len(result.embedding or []))


asyncio.run(main())

Counting tokens before sending a request:

import asyncio

from zhivex_ai import create_gemini


async def main() -> None:
    gemini = create_gemini()
    counts = await gemini.tokens().count(
        model_id="gemini-2.5-flash",
        prompt="Summarize this in one line.",
    )

    print(counts.total_tokens)


asyncio.run(main())

Using Gemini explicit context caching:

import asyncio

from zhivex_ai import create_gemini, generate_text


async def main() -> None:
    gemini = create_gemini()
    cache = await gemini.caches().create(
        {
            "model": "models/gemini-2.5-flash",
            "displayName": "Product docs",
            "contents": [{"role": "user", "parts": [{"text": "Long reusable context..."}]}],
            "ttl": "3600s",
        }
    )
    result = await generate_text(
        model=gemini.native.language_model("gemini-2.5-flash"),
        prompt="Summarize the cached docs in three bullets.",
        provider_options={"cached_content": cache.name},
    )

    print(result.text)


asyncio.run(main())

Managing Gemini File Search stores:

import asyncio

from zhivex_ai import create_gemini


async def main() -> None:
    gemini = create_gemini()
    store = await gemini.file_search_stores().create(display_name="Docs")
    operation = await gemini.file_search_stores().upload(
        file_search_store_name=store.name,
        data=b"%PDF-1.4...",
        filename="manual.pdf",
        media_type="application/pdf",
    )

    await gemini.file_search_stores().wait_operation(operation.name)


asyncio.run(main())

Managing OpenAI Vector Stores / File Search stores:

import asyncio

from zhivex_ai import create_openai


async def main() -> None:
    openai = create_openai()
    store = await openai.file_search_stores().create(display_name="Docs")
    operation = await openai.file_search_stores().upload(
        file_search_store_name=store.name,
        data=b"%PDF-1.4...",
        filename="manual.pdf",
        media_type="application/pdf",
        custom_metadata=[{"key": "lang", "value": "en"}],
    )

    await openai.file_search_stores().wait_operation(operation.name)


asyncio.run(main())

Using OpenAI uploads and images:

import asyncio

from zhivex_ai import create_openai


async def main() -> None:
    openai = create_openai()
    file = await openai.uploads().upload_bytes(
        data=b'{"messages":[{"role":"user","content":"hello"}]}\n',
        filename="batch.jsonl",
        mime_type="application/jsonl",
        purpose="batch",
    )
    image = await openai.images().generate(prompt="A paper sketch of a transit map", model="gpt-image-2")

    print(file.id)
    print(image.images[0].b64_json is not None)


asyncio.run(main())

Passing inline audio to Gemini text generation:

import asyncio

from zhivex_ai import FilePart, ModelMessage, TextPart, create_gemini, generate_text


async def main() -> None:
    gemini = create_gemini()
    result = await generate_text(
        model=gemini("gemini-2.5-flash"),
        messages=[
            ModelMessage(
                role="user",
                parts=[
                    FilePart(
                        data="SUQzBAAAAAAA...",
                        media_type="audio/mpeg",
                        filename="call.mp3",
                    ),
                    TextPart(text="Summarize the call in five bullets."),
                ],
            )
        ],
    )

    print(result.text)


asyncio.run(main())

Passing a Vertex-hosted file reference:

import asyncio

from zhivex_ai import FilePart, ModelMessage, TextPart, create_vertex, generate_text


async def main() -> None:
    vertex = create_vertex()
    result = await generate_text(
        model=vertex("gemini-2.5-flash"),
        messages=[
            ModelMessage(
                role="user",
                parts=[
                    FilePart(file_uri="gs://my-bucket/meeting.mp4", media_type="video/mp4"),
                    TextPart(text="Extract the main decisions from this meeting."),
                ],
            )
        ],
    )

    print(result.text)


asyncio.run(main())

Using Google native media and long-running clients:

import asyncio

from zhivex_ai import create_gemini


async def main() -> None:
    gemini = create_gemini()

    image = await gemini.images().generate(
        model="gemini-3.1-flash-image",
        prompt="A clean product diagram of a solar microgrid.",
    )
    operation = await gemini.videos().generate(
        model="veo-3.1-generate-preview",
        prompt="A slow cinematic flyover of that microgrid at sunrise.",
    )
    music = await gemini.media().generate_music(
        model="lyria-3-pro-preview",
        prompt="A 30-second optimistic ambient technology track.",
    )

    print(image.images[0].b64_json is not None)
    print(operation.name)
    print(music.media[0].media_type)


asyncio.run(main())

Audio

import asyncio
from pathlib import Path

from zhivex_ai import AudioInput, create_openai, transcribe_audio


async def main() -> None:
    openai = create_openai()
    audio = AudioInput(
        data=Path("sample.wav").read_bytes(),
        media_type="audio/wav",
        filename="sample.wav",
    )

    result = await transcribe_audio(
        model=openai.transcription_model("gpt-4o-transcribe"),
        audio=audio,
    )

    print(result.text)


asyncio.run(main())

import asyncio
import wave
from pathlib import Path

from zhivex_ai import create_gemini, generate_speech


def save_wave(path: Path, pcm: bytes, *, channels: int = 1, rate: int = 24_000, sample_width: int = 2) -> None:
    with wave.open(str(path), "wb") as wav_file:
        wav_file.setnchannels(channels)
        wav_file.setsampwidth(sample_width)
        wav_file.setframerate(rate)
        wav_file.writeframes(pcm)


async def main() -> None:
    gemini = create_gemini()
    result = await generate_speech(
        model=gemini.speech_model("gemini-2.5-flash-preview-tts"),
        input="Zhivex AI SDK makes provider switching easier.",
        voice="Kore",
    )
    save_wave(Path("speech.wav"), result.audio)


asyncio.run(main())

Agent runtime

import asyncio
from pydantic import BaseModel, ConfigDict

from zhivex_ai import (
    Agent,
    create_openai,
    handoff_to,
    run_agent,
    tool,
)


class DelegateInput(BaseModel):
    model_config = ConfigDict(extra="forbid")
    task: str


async def main() -> None:
    openai = create_openai()
    researcher = Agent(
        name="researcher",
        instructions="Answer delegated research questions directly.",
        model=openai("gpt-5.4-mini"),
    )
    triage = Agent(
        name="triage",
        instructions="Delegate research work to the researcher agent.",
        model=openai("gpt-5.4-mini"),
        tools={
            "delegate": tool(
                name="delegate",
                schema=DelegateInput,
                execute=lambda input: handoff_to("researcher", input=input.task),
            )
        },
        subagents={"researcher": researcher},
    )

    result = await run_agent(agent=triage, prompt="Research the Apollo migration status.")

    print(result.text)
    print(result.orchestration_path)


asyncio.run(main())

If you want Gemini research with provider-native web search in an agent run, put the agent on a native Gemini model first:

from zhivex_ai import create_gemini

gemini = create_gemini()
triage.model = gemini.native.language_model("gemini-3.1-flash-preview")
result = await run_agent(
    agent=triage,
    prompt="Research the Apollo migration status.",
    provider_options={"google_search": True},
)

Built-in Gemini tools can also be configured directly:

result = await generate_text(
    model=gemini.native.language_model("gemini-3.1-flash-preview"),
    prompt="Research this page and show your work.",
    provider_options={
        "google_search": {"excludeDomains": ["example.com"]},
        "url_context": {},
        "code_execution": True,
    },
)

Hosted tools can now live directly inside tools={...} on native models, alongside callable local tools:

import asyncio

from pydantic import BaseModel, ConfigDict

from zhivex_ai import create_openai, generate_text, openai_web_search_tool, tool


class WeatherInput(BaseModel):
    model_config = ConfigDict(extra="forbid")
    city: str


async def main() -> None:
    openai = create_openai()

    result = await generate_text(
        model=openai.native.language_model("gpt-5.4-mini"),
        prompt="Compare today's weather in Buenos Aires with what is happening in the news.",
        tools={
            "weather": tool(
                name="weather",
                schema=WeatherInput,
                execute=lambda input: {"city": input.city, "forecast": "18C and cloudy"},
            ),
            "search": openai_web_search_tool(search_context_size="high"),
        },
    )

    print(result.text)


asyncio.run(main())

Remote MCP approval responses also round-trip through assistant messages with a provider-data part:

from zhivex_ai import assistant, openai_mcp_approval_response, user

messages = [
    assistant([openai_mcp_approval_response(approval_request_id="apr_123", approve=True)]),
    user("Continue with the approved MCP call."),
]

OpenAI and Azure OpenAI also expose beta typed provider-data payloads and parse helpers:

from zhivex_ai import (
    OpenAIMcpApprovalRequest,
    parse_openai_provider_data_part,
    provider_data_part,
)

part = provider_data_part(
    "openai",
    OpenAIMcpApprovalRequest(
        id="apr_123",
        arguments='{"query":"apollo"}',
        name="docs_search",
        server_label="Docs",
    ),
)

parsed = parse_openai_provider_data_part(part)
assert parsed is not None
assert parsed.type == "mcp_approval_request"

You can also continue OpenAI Responses API workflows without manually threading raw IDs:

from zhivex_ai import (
    get_openai_response_id,
    openai_response_options,
)

first = await generate_text(
    model=openai.native.language_model("gpt-5.4-mini"),
    prompt="Start a multi-turn responses workflow.",
)

follow_up = openai_response_options(previous_response=first)
assert get_openai_response_id(first) is not None

The UI helpers now preserve provider-managed control traffic as provider-data chunks as well, so to_ui_message_stream(...) can surface MCP approval requests, response references, and other typed provider-data events to frontend consumers.

When these approval requests appear inside run_agent(...) or stream_agent(...), the runtime reuses approval_policy to emit AgentToolApprovalEvent events, append the provider-specific approval response, and continue the loop. This provider-managed approval path is currently beta and limited to OpenAI and Azure OpenAI; other providers may expose hosted-tool metadata in the support matrix before they share this same approval/runtime integration.

See examples/text/native_hosted_tools.py for a compact mixed local + hosted tool example, and examples/agents/provider_managed_approvals.py for the matching OpenAI/Azure remote MCP approval flow with stream_agent(...).

Gateway fallback routing

import asyncio

from zhivex_ai import (
    GatewayConfig,
    GatewayMessage,
    GatewayModelTarget,
    create_anthropic,
    create_gateway,
    create_openai,
)


async def main() -> None:
    gateway = create_gateway(
        GatewayConfig(
            adapters={
                "openai": create_openai(),
                "anthropic": create_anthropic(),
            }
        )
    )

    result = await gateway.generate(
        messages=[GatewayMessage(role="user", content="Say hello in one sentence.")],
        primary=GatewayModelTarget(provider="openai", model_id="gpt-5.4-mini"),
        fallbacks=[GatewayModelTarget(provider="anthropic", model_id="claude-sonnet-4-20250514")],
    )

    print(result.text)
    print(result.provider_used, result.model_used)


asyncio.run(main())

Provider Factories

The package currently exposes:

• create_openai()
• create_azure_openai()
• create_anthropic()
• create_gemini()
• create_vertex()
• create_vllm()
• create_bedrock()
• create_openrouter()
• create_qwen()
• create_kimi()
• create_ollama()

Every factory now returns a ProviderBundle.

Portable usage:

• provider("model-id")
• provider.language_model("model-id")
• provider.embedding_model(...)
• provider.transcription_model(...)
• provider.speech_model(...)
• provider.grounded_language_model(...)

Native usage:

• provider.native.language_model("model-id")
• provider.native.embedding_model(...)
• provider.native.transcription_model(...)
• provider.native.speech_model(...)
• provider.native.grounded_language_model(...)
• provider.native.realtime_model(...)

Portable model construction fails fast when the provider does not hold the portable badge. That is intentional: the default path is the portability promise, and provider.native is the explicit escape hatch.

OpenAI-compatible providers such as OpenRouter, Qwen, Ollama, and vLLM reuse normalized adapter paths internally. Qwen and vLLM participate in the tier-1 portable story; Ollama and OpenRouter remain outside the tier-1 portable contract. Kimi/Moonshot uses its own native Chat Completions adapter because the official Kimi API documents /v1/chat/completions, Files, Batch, token estimation, and Formulas as the current production surfaces.

Azure OpenAI supports API key authentication and Microsoft Entra ID authentication through the versionless /openai/v1 route. API key usage reads AZURE_OPENAI_API_KEY plus AZURE_OPENAI_ENDPOINT; Entra ID usage passes a token or token provider explicitly and is mutually exclusive with API keys.

from azure.identity import DefaultAzureCredential, get_bearer_token_provider
from zhivex_ai import create_azure_openai

token_provider = get_bearer_token_provider(
    DefaultAzureCredential(),
    "https://ai.azure.com/.default",
)

provider = create_azure_openai(
    endpoint="https://YOUR-RESOURCE-NAME.openai.azure.com",
    entra_token_provider=token_provider,
)

The SDK does not depend on azure-identity; install it in your application if you want to use DefaultAzureCredential.

Qwen/Alibaba Cloud Model Studio native usage:

import asyncio

from zhivex_ai import create_qwen, generate_text, qwen_web_search_tool


async def main() -> None:
    provider = create_qwen(region="us")  # intl, us, or cn
    result = await generate_text(
        model=provider.native.language_model("qwen3.7-plus"),
        prompt="Summarize the latest Qwen hosted tool surface.",
        tools={"search": qwen_web_search_tool()},
    )
    print(result.text)


asyncio.run(main())

create_qwen() reads QWEN_API_KEY or the official DASHSCOPE_API_KEY. By default it targets Alibaba Cloud Model Studio's Singapore-compatible endpoint with region="intl"; use region="us" for US Virginia or region="cn" for China Beijing. Pass base_url=... or responses_base_url=... only for custom gateways or advanced endpoint overrides. Use provider("qwen-model") for the portable text/streaming/tools/structured-output path, and provider.native.* for hosted tools/MCP, raw Responses settings, Files, Batch, Qwen3-ASR, and DashScope TTS.

See examples/text/qwen_native.py for a fuller provider-specific example covering Qwen text, hosted web search, embeddings, optional Qwen3-ASR, and optional Qwen3-TTS.

vLLM usage targets its OpenAI-compatible server:

import asyncio

from zhivex_ai import create_vllm, generate_text


async def main() -> None:
    provider = create_vllm(base_url="http://localhost:8000/v1")
    result = await generate_text(
        model=provider("NousResearch/Meta-Llama-3-8B-Instruct"),
        prompt="Explain vLLM in one sentence.",
    )
    print(result.text)


asyncio.run(main())

create_vllm() reads VLLM_API_KEY and VLLM_BASE_URL; local development can omit the API key and use the default compatibility token vllm. The tier-1 guarantee covers the SDK primitives vLLM exposes through OpenAI-compatible routes: text generation, streaming, structured output/tools, embeddings, transcription, and realtime ASR. Model-specific tasks still matter: embeddings require an embedding model, transcription/realtime require ASR-capable vLLM setup, and vLLM custom endpoints such as tokenize, rerank, classify, and score are intentionally outside the SDK surface for now.

Kimi/Moonshot native usage:

import asyncio

from zhivex_ai import ImagePart, ReasoningConfig, create_kimi, generate_text, user


async def main() -> None:
    kimi = create_kimi()  # MOONSHOT_API_KEY, then KIMI_API_KEY

    result = await generate_text(
        model=kimi.native.language_model("kimi-k2.6"),
        messages=[
            user(
                [
                    ImagePart(image="data:image/png;base64,..."),
                ]
            )
        ],
        reasoning=ReasoningConfig(effort="none"),
    )
    print(result.text)


asyncio.run(main())

Kimi files, batch jobs, token estimation, and official tools are native-only:

kimi = create_kimi()

uploaded = await kimi.files().upload(
    data=b'{"custom_id":"1","method":"POST","url":"/v1/chat/completions","body":{"model":"kimi-k2.6","messages":[{"role":"user","content":"hi"}]}}\n',
    filename="batch.jsonl",
    media_type="application/jsonl",
    purpose="batch",
)

batch = await kimi.batches().create(
    {
        "input_file_id": uploaded.id,
        "endpoint": "/v1/chat/completions",
        "completion_window": "24h",
    }
)

tokens = await kimi.tokens().count(model_id="kimi-k2.6", prompt="hello")
tools = await kimi.formulas().toolset(["moonshot/web-search:latest"])

Local Ollama usage follows the same native escape hatch:

import asyncio

from zhivex_ai import create_ollama, generate_text


async def main() -> None:
    provider = create_ollama(base_url="http://localhost:11434/v1")
    result = await generate_text(
        model=provider.native.language_model("llama3.2"),
        prompt="Explain Zhivex AI SDK in one sentence.",
    )
    print(result.text)


asyncio.run(main())

For local Ollama installs, api_key="ollama" is the default compatibility token and is usually sufficient. Set OLLAMA_API_KEY only when your Ollama endpoint is behind a proxy or hosted gateway that expects authentication.

Adapters may also expose optional factories such as:

• provider.native.realtime_model("gpt-realtime-2")
• provider.files()
• provider.images()
• provider.videos()
• provider.media()
• provider.uploads()
• provider.tokens()
• provider.file_search_stores()
• provider.batches()
• provider.interactions()
• provider.formulas()
• provider.responses()
• provider.conversations()

OpenAI and Azure OpenAI providers may additionally expose low-level lifecycle clients:

• provider.responses() for raw Responses API operations such as create, create_background, retrieve, wait, cancel, compact, and list_input_items
• provider.conversations() for raw Conversations API operations such as create, retrieve, update, create_item, and list_items
• provider.file_search_stores() for Vector Store / File Search lifecycle operations such as create, update, search, upload, list_documents, get_operation, and wait_operation
• provider.images() for standalone image generation and edit flows
• provider.uploads() for multi-part uploads that complete into reusable OpenAI Files
• provider.moderations() for raw Moderations API requests
• provider.batches() for raw Batch API lifecycle operations such as create, retrieve, list, and cancel
• provider.containers() for container lifecycle and container-file management
• provider.skills() for skill lifecycle and skill-version management

OpenAI helper builders cover the modern hosted-tool surface, including file search filters, code-interpreter containers, shell environments, MCP servers, inline skills, skill references, custom tools, namespaces, and tool search.

Capability Matrix

The canonical matrix now lives in runtime metadata:

• provider.portable_support
• provider.native_support
• provider.tier
• default_model_catalog keeps recommendation metadata for current reference models such as OpenAI GPT-5.5/GPT-5.4 plus GPT Image 2 and GPT Realtime 2, Azure OpenAI GPT-5.5/realtime/image/embedding IDs, Claude Fable 5/Opus 4.8/Sonnet 4.6/Haiku 4.5, Gemini 3.5 Flash/Live Translate and Gemini 3.1 media/live IDs, Vertex Gemini, Bedrock Claude 4.x/Nova, Qwen 3.7/3.6 plus Qwen embedding/rerank/audio IDs, and Kimi. It is guidance for model selection, not a separate execution path.

To regenerate the markdown tables used above:

.venv/bin/python scripts/generate_support_matrix.py

Notes:

• provider("model-id") is shorthand for the portable namespace.
• provider.native.* is the only place where provider-specific request shapes belong.
• Some providers support a capability only for specific model IDs even when the adapter exposes the factory.
• create_gemini().files() exposes the Gemini Files API. create_vertex() does not expose a hosted files client; on Vertex, pass FilePart(file_uri="gs://...") or inline media instead.
• create_anthropic().tokens() exposes Anthropic message token counting.
• anthropic_web_search_tool(...), anthropic_mcp_server(...), and anthropic_code_execution_tool(...) build the current hosted-tool payloads for Claude-native runs.
• create_gemini().tokens() and create_vertex().tokens() expose token counting clients.
• create_gemini().caches() exposes Gemini explicit context caching through cachedContents; pass the returned cache name with provider_options={"cached_content": cache.name} or provider_options={"cachedContent": cache.name}.
• create_gemini().file_search_stores() exposes Gemini File Search store management.
• embed_content(...) and embed_content_many(...) accept text plus TextPart, ImagePart, and FilePart values for Gemini Embedding 2 style multimodal embeddings; embed(...) and embed_many(...) remain text-compatible.
• create_gemini().images() covers Gemini/Nano Banana image models such as gemini-3.1-flash-image and gemini-3-pro-image through generateContent, plus Imagen 4 models through predict.
• create_vertex().images() mirrors Google image routes through Vertex publisher model endpoints.
• create_gemini().videos() and create_vertex().videos() expose Veo long-running operation creation, polling, and download helpers, including the current veo-3.1-* model IDs where available.
• create_gemini().media() and create_vertex().media() expose Lyria-style native audio/music generation where the Google model route supports it, including lyria-3-pro-preview and lyria-3-clip-preview.
• create_gemini().realtime_model("gemini-3.5-live-translate-preview") exposes Gemini Live Translate with typed RealtimeSessionConfig(translation_target_language_code="es", translation_echo_target_language=True, input_audio_media_type="audio/pcm;rate=16000", output_audio_media_type="audio/pcm") setup. Live Translate is audio-only; text input, tools, and instructions fail fast for that model.
• create_gemini().batches() exposes Gemini Batch API generation and embedding jobs.
• create_gemini().interactions() exposes Gemini Interactions and Deep Research polling/streaming helpers as a raw beta client. Deep Research payloads default to background storage.
• create_openai().file_search_stores() exposes OpenAI Vector Store / File Search management.
• create_azure_openai().file_search_stores() exposes Azure OpenAI Vector Store / File Search management through the versionless /openai/v1 endpoint and works with either API key or Entra ID authentication.
• create_vertex() now exports native grounding helpers such as vertex_google_search_tool(...), vertex_google_maps_tool(...), vertex_vertex_ai_search_tool(...), and vertex_external_search_tool(...).
• create_vertex().native.language_model(...) and create_vertex().native.grounded_language_model(...) also accept provider_options={"vertex_ai_search": {...}} and provider_options={"external_search": {...}}, which are normalized into the Vertex tool payloads automatically.
• create_openai().images() and create_openai().uploads() expose standalone OpenAI Images and Uploads APIs, including GPT Image 2 via model="gpt-image-2".
• create_openai().containers() and create_openai().skills() expose the raw OpenAI Containers and Skills APIs.
• OpenAI/Azure Sora or video-generation lifecycle clients are intentionally not exposed in this SDK release.
• create_vertex().realtime_model(...).create_browser_token() is intentionally unsupported. Vertex realtime sessions use server-side authentication instead of OpenAI/Gemini-style ephemeral browser tokens in this SDK.
• OpenAI and Azure OpenAI browser bootstrap now follows the official realtime/client_secrets flow, while Gemini browser tokens still come from v1alpha/authTokens.
• Realtime sessions emit realtime-response-complete when a model turn finishes and reserve realtime-end for actual session shutdown or transport closure.
• Gemini and Vertex speech generation return PCM audio in the current examples, so the demo writes a .wav container around the bytes.

Why not use provider SDKs directly?

Using provider SDKs directly is totally reasonable when:

• you only target one provider
• you are comfortable rewriting message, tool, and streaming logic per vendor
• you do not need fallback routing or a shared abstraction layer

Zhivex AI SDK is a better fit when:

• you want one contract across multiple model vendors
• you expect to switch providers over time
• you want tools, structured output, caching, telemetry, and routing to live above the provider layer
• you want application code that reads the same whether the model is OpenAI, Anthropic, Gemini, or local

Middleware

Zhivex AI SDK includes middleware helpers similar to the TypeScript SDK:

• wrap_language_model(...)
• create_telemetry_middleware(...)
• create_cached_generate_middleware(...)
• create_in_memory_generate_cache()
• create_file_generate_cache(...)
• create_circuit_breaker_middleware(...)

These let you keep cross-cutting concerns outside provider adapters and application prompts.

For production logging, request correlation, gateway attempt tracing, and OpenTelemetry guidance, see docs/OBSERVABILITY.md.

UI And Transport

The Python SDK now includes helpers for UI and transport-oriented flows:

• to_ui_message(...), to_ui_messages(...)
• from_ui_message(...), from_ui_messages(...)
• serialize_ui_message(...), deserialize_ui_message(...)
• parse_ui_message_request(...)
• create_ui_message_json_response(...)
• create_ui_message_lines_response(...)
• to_sse_stream(...), to_sse_response(...)
• to_text_stream_response(...)
• to_ui_message_stream_response(...)

These are useful when wiring the SDK into web servers, SSE endpoints, or custom chat frontends.

For production-style FastAPI integration patterns, see PRODUCTION_APIS.md and the reference apps in examples/integrations/.

Agents

The Python SDK now exposes an agent-first runtime on top of the core model contract:

• Agent(...)
• AgentRuntime(...)
• AgentRegistry(...)
• ToolRegistry(...)
• AgentSession
• run_agent(...)
• resume_agent(...)
• stream_agent(...)
• create_in_memory_agent_memory_store()
• create_in_memory_checkpoint_store()
• create_in_memory_agent_run_store()
• create_sqlite_agent_memory_store(...)
• create_sqlite_checkpoint_store(...)
• create_sqlite_agent_run_store(...)
• create_postgres_agent_memory_store(...)
• create_postgres_checkpoint_store(...)
• create_postgres_agent_run_store(...)
• create_otel_agent_observer()
• create_agent_trace_artifact(...)
• summarize_agent_trace(...)
• create_agent_run_snapshot(...)
• replay_agent_run(...)
• run_agent_evaluation(...)
• create_agent_evaluation_report(...)
• create_safety_policy(...)
• apply_safety_policy_to_agent(...)
• load_agent_session(...)
• ApprovalDecision, ToolApprovalRequest
• GuardrailResult, InputGuardrailRequest, OutputGuardrailRequest
• permission_allowlist_approval_policy(...)
• input and output guardrails on Agent(...)
• handoff_to(...)
• create_subagent_tool(...)
• prepare_subagents_for_agent(...)
• run_agent_group(...)
• remote_tool(...)
• skill(...)
• load_skill(...)
• discover_skills(...)
• discover_mcp_tools(...)
• mcp_stdio_server(...)
• mcp_http_server(...)
• create_mcp_tool_registry(...)

This layer is intended for stateful, tool-using, multi-agent assistants where you want executable handoffs, native subagent tools, shared sessions, transcript + summary memory, approval hooks, replay/evaluation, traces, durable run state, and MCP-backed tool registries without rewriting the lower-level loop yourself.

For production semantics, persistence, approvals, tool registries, event ordering, and recovery guidance, see docs/AGENTS.md and docs/PRODUCTION.md.

Declarative workflows are available when the coordination pattern is known ahead of time:

from zhivex_ai import SequentialAgent, WorkflowStep

pipeline = SequentialAgent(
    name="loan_pipeline",
    steps=[
        WorkflowStep("extract", extractor, prompt="Extract the application", output_key="application"),
        WorkflowStep("validate", validator, input_template="Validate {application}", output_key="validation"),
        WorkflowStep("decide", decider, input_template="Decide with {application} and {validation}"),
    ],
)

result = await pipeline.run()

Use ParallelAgent for fan-out research and LoopAgent for bounded refinement loops. Workflow steps share session.state; output_key writes the step text into state, and input_template reads state keys with Python format placeholders. Workflow APIs are beta; see docs/WORKFLOWS.md for structured outputs, resume patterns, error policy, replay, and evaluation guidance.

For fuller business-workflow references, see examples/agents/small_business_loan_agent.py, examples/agents/hr_candidate_selection_agent.py, and the focused workflow examples under examples/agents/. They model regulated financial review, HR candidate selection, structured step validation, workflow resume, document artifacts, and research reports. The SDK owns orchestration primitives; the examples keep credit policy, hiring policy, pricing/scoring, persistence, approval UI, ATS integrations, artifact storage, and compliance systems application-owned behind replaceable interfaces.

Durable run state can be attached directly to an agent:

from zhivex_ai import Agent, create_in_memory_agent_run_store, run_agent

store = create_in_memory_agent_run_store()
agent = Agent(name="assistant", model=model, run_store=store)
result = await run_agent(agent=agent, prompt="Draft a reply", idempotency_key="reply-1")
state = await store.load(result.run_id)

Safety policies compose approval, redaction, and budget defaults without mutating the original agent:

from zhivex_ai import apply_safety_policy_to_agent, create_safety_policy

safe_agent = apply_safety_policy_to_agent(agent, create_safety_policy(preset="review_sensitive"))

Evaluation and trace helpers work from persisted state:

from zhivex_ai import create_agent_trace_artifact, replay_agent_run, summarize_agent_trace

trace = create_agent_trace_artifact(state)
timeline = replay_agent_run(state)
summary = summarize_agent_trace(state)

Agent skills are also available across the agent runtime. These are provider-agnostic workflow packs that inject task-specific instructions and optional tool dependencies before a run starts. They are distinct from the raw OpenAI Skills API:

• Agent(..., skills=...) activates provider-agnostic runtime skills
• skill(...), load_skill(...), and discover_skills(...) help define or load skills from SKILL.md
• set_agent_session_skills(...), get_agent_session_skills(...), and clear_agent_session_skills(...) manage sticky session skills explicitly
• provider.skills() remains the native OpenAI lifecycle client for hosted OpenAI skills
• activated skills stick to the agent session through session.metadata["sticky_skills"]
• the runtime emits AgentSkillActivatedEvent and AgentSkillSkippedEvent for observability

Runtime skills follow the Codex-style SKILL.md layout: frontmatter with name and description, instruction body, and optional agents/openai.yaml metadata for display text, implicit-invocation policy, and MCP tool dependencies.

The SDK now also includes a beta skill-package layer for Anthropic-style packaged skills. This adds skill.yaml, installable skill packages, a static HTTP registry flow, direct run_skill(...), artifacts, and the zhivex-skills CLI. The packaged-skill APIs are:

• load_skill_package(...)
• validate_skill(...)
• install_skill(...)
• list_installed_skills(...)
• run_skill(...)
• publish_skill(...)

Packaged skills can declare:

• versioned entrypoints
• local Python or binary dependencies
• produced artifacts
• explicit read/write/network permissions

The first official packaged skill is the beta docx skill under zhivex_ai/official_skills/docx, designed around python-docx.

The runtime also supports production-oriented policy metadata:

• priority to resolve competing implicit skills
• triggers and anti_triggers for deterministic activation rules
• allowed_providers and allowed_models to constrain where a skill can run
• persist_to_session to opt out of sticky reuse
• dependency_failure_mode set to "skip" or "fail"

import asyncio

from zhivex_ai import (
    Agent,
    clear_agent_session_skills,
    create_agent_session,
    create_openai,
    get_agent_session_skills,
    run_agent,
    set_agent_session_skills,
    skill,
)


async def main() -> None:
    openai = create_openai()
    release_notes = skill(
        name="release-notes",
        description="Use when a user asks for changelog summaries or release notes.",
        instructions="Write highlights, breaking changes, and migration notes when needed.",
    )
    agent = Agent(
        name="assistant",
        instructions="You are a careful SDK assistant.",
        model=openai("gpt-5.4-mini"),
        skills={"release-notes": release_notes},
    )
    session = create_agent_session()

    set_agent_session_skills(session, "release-notes")
    result = await run_agent(agent=agent, session=session, prompt="Summarize the latest SDK updates.")
    print(result.text)
    print(result.session.metadata["active_skills"])
    print(get_agent_session_skills(session))
    clear_agent_session_skills(session)


asyncio.run(main())

For new MCP integrations, prefer the higher-level helpers:

import asyncio

from zhivex_ai import Agent, create_mcp_tool_registry, create_openai, mcp_stdio_server, run_agent


async def main() -> None:
    async with await create_mcp_tool_registry(
        mcp_stdio_server(
            name="fs",
            command="npx",
            args=["-y", "@modelcontextprotocol/server-filesystem", "."],
        )
    ) as tools:
        openai = create_openai()
        agent = Agent(
            name="assistant",
            instructions="Use the filesystem MCP tools when needed.",
            model=openai("gpt-5.4-mini"),
            tools=tools,
        )

        result = await run_agent(agent=agent, prompt="List the Python files in the current directory.")
        print(result.text)


asyncio.run(main())

discover_mcp_tools(...) is still available when you want raw tool definitions or full control over prefixes and registry composition. ToolRegistry also supports async with so MCP-backed runtimes can be closed cleanly after use.

Examples

See examples/README.md for the full list. Highlights:

• Text: openai_text.py, stream_text.py, structured_output.py
• Local Ollama: ollama_text.py
• Local vLLM: vllm_text.py
• Agents: agent_basic.py, stream_agent.py, mcp_tools.py
• Agent skills: skills.py
• Realtime: openai_realtime.py, gemini_realtime.py, live_agent_realtime.py
• Audio: transcribe_audio.py, generate_speech.py
• Integrations: ui_messages.py, http_responses.py, gateway_fallback.py
• Production: fastapi_agent_api.py, worker_resume.py

For real provider validation, the repo also includes a live smoke runner:

export ZHIVEX_SMOKE_OPENAI_MODEL=your-openai-model
export ZHIVEX_SMOKE_GEMINI_MODEL=your-gemini-model
export ZHIVEX_SMOKE_ANTHROPIC_MODEL=your-anthropic-model
export ZHIVEX_SMOKE_AZURE_OPENAI_MODEL=your-azure-openai-deployment
export ZHIVEX_SMOKE_VERTEX_MODEL=your-vertex-model
export ZHIVEX_SMOKE_QWEN_MODEL=your-qwen-model
export ZHIVEX_SMOKE_KIMI_MODEL=your-kimi-model
export ZHIVEX_SMOKE_VLLM_MODEL=your-vllm-model
export ZHIVEX_SMOKE_OLLAMA_MODEL=your-local-ollama-model
export ZHIVEX_SMOKE_QWEN_REGION=intl
make smoke

It only runs providers that have the required credentials and model IDs configured, and you can scope it with ZHIVEX_SMOKE_PROVIDERS=openai,anthropic,azure-openai,gemini,vertex,qwen,kimi,vllm. Tier-1 setup details live in docs/providers/tier-1.md. Optional Google media smoke checks are gated behind ZHIVEX_SMOKE_GOOGLE_MEDIA=1 and model IDs such as ZHIVEX_SMOKE_GEMINI_IMAGE_MODEL, ZHIVEX_SMOKE_GEMINI_VIDEO_MODEL, ZHIVEX_SMOKE_GEMINI_MEDIA_MODEL, ZHIVEX_SMOKE_VERTEX_IMAGE_MODEL, ZHIVEX_SMOKE_VERTEX_VIDEO_MODEL, and ZHIVEX_SMOKE_VERTEX_MEDIA_MODEL. Ollama smoke runs default to http://localhost:11434/v1 and can be redirected with ZHIVEX_SMOKE_OLLAMA_BASE_URL. Qwen smoke uses DASHSCOPE_API_KEY or QWEN_API_KEY, supports ZHIVEX_SMOKE_QWEN_BASE_URL and ZHIVEX_SMOKE_QWEN_RESPONSES_BASE_URL overrides, and can optionally validate embeddings, ASR, and TTS with ZHIVEX_SMOKE_QWEN_EMBEDDING_MODEL, ZHIVEX_SMOKE_QWEN_ASR_MODEL plus ZHIVEX_SMOKE_QWEN_ASR_AUDIO_PATH, and ZHIVEX_SMOKE_QWEN_TTS_MODEL. Kimi smoke uses MOONSHOT_API_KEY or KIMI_API_KEY, with optional MOONSHOT_BASE_URL or ZHIVEX_SMOKE_KIMI_BASE_URL.

If realtime examples fail on macOS with ssl.SSLCertVerificationError: CERTIFICATE_VERIFY_FAILED, the issue is usually the local Python certificate bundle rather than the SDK. Two practical fixes are:

SSL_CERT_FILE="$(".venv/bin/python" -c 'import certifi; print(certifi.where())')" \
GOOGLE_API_KEY=... \
.venv/bin/python examples/realtime/gemini_realtime.py

or, for a permanent fix with the official python.org installer:

"/Applications/Python 3.14/Install Certificates.command"

License

MIT. See LICENSE.